4. (a) Consider a linear regression problem , with a training set having m examples . Suppose that we wish to minimize the mean fifth degree error (loss function) given by:

i. Derive the equation to calculate the gradient with respect to the parameters w₁ and w₀. (6 marks) ii. Write the pseudo-code of the gradient descent algorithm for this problem. (6 marks)

(b) Consider the following set of points: {(-2, -1), (1, 1), (3, 2)} i. Find the least square regression line for the given data points. (5 marks) ii. Plot the given points and the regression line in the same rectangular system of axes. (5 marks)

(c) What is the use of basis function in linear regression? (5 marks)

y = W10 + wo (21, y1), 12, y2), ..., Im, Ym) Loss = (u: ) y = W10 + wo (21, y1), 12, y2), ..., Im, Ym) Loss = (u: )